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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

Computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Unveiling the barriers to digital transformation in higher education institutions: a systematic literature review

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Discover Education 2025年第1期4卷 1-41页

作者： Singun, Amando Jr. College of Computing and Information Sciences University of Technology and Applied Sciences PO Box: 74 Al Khuwair Muscat PC: 133 Oman

This study investigates the challenges hindering the implementation of Digital Transformation (DT) in Higher Education Institutions (HEIs) by thoroughly reviewing the literature. It identifies multiple dimensions and subdimensions of these barriers to offer valuable insights to help HEIs navigate their transformation processes successfully. By doing so, they can effectively address the changing requirements of students, faculty, administration, and other stakeholders in an increasingly digital environment. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, synthesizing data from twenty (20) relevant peer-reviewed articles published between 2019 and 2024. The author employed NVivo and Zotero as methodological tools for coding, thematic analysis, and text mining to derive insights from the selected articles, thereby facilitating the development of a Concept-Centric Matrix of the Barriers to Digital Transformation (DT) in Higher Education Institutions (HEIs). The result of this study is summarized in a Concept-centric Matrix on Barriers to DT in HEIs consisting of nine (9) dimensions such as (i) Digital Vision, Strategy, and Policies;(ii) Digital Leadership and Management;(iii) Digital Organization;(iv) Digital Resources;(v) Digital Competence;(vi) Digital Stakeholder Management;(vii) Digital Culture;(viii) Digital Academic;and (ix) Digital Ethics. Each dimension has subdimensions of barriers (n = 80). Despite various sectors anticipating significant DT, many HEIs feel inadequately prepared to adapt because of the barriers to its implementation. The ongoing DT within HEIs reveals a complex landscape marked by various intertwined barriers that necessitate a strategic reevaluation and a commitment to fostering an inclusive, responsive educational environment. © The Author(s) 2025.

关键词： Barriers Concept-centric matrix Digital transformation Education HEI Higher education Higher education institutions NVivo PRISMA Systematic literature review University

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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

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IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange Data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

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An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks

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Multimedia Tools and Applications 2025年第8期84卷 4393-4434页

作者： Lawrence, Morolake Oladayo Jimoh, Rasheed Gbenga Yahya, Waheed Babatunde Department of Computer Science Faculty of Computing & Applied Sciences Baze University Abuja Nigeria Department of Computer Science Faculty of Communication & Information Sciences University of Ilorin Kwara State Ilorin Nigeria Department of Statistics Faculty of Physical Sciences University of Ilorin Kwara State Ilorin Nigeria

Cancer is one of the most devastating health conditions in the world. In the diagnosis and treatment of the various forms of cancer illness, studies have shown that early detection of the cancer by clinical methods usually takes a considerable lengthy time. This informs searching for an alternative non-clinical diagnosis of cancer cases using microarray technology. Therefore, this study develops an efficient feature selection and classification method for high-dimensional microarray cancer data by combining Genetic Algorithms (GA) and Deep Belief Networks (DBN). The study employed a GA for selecting the most informative gene biomarker and DBN for the classification of biological samples. In a Monte Carlo experiment, the simulated and real-life microarray datasets were partitioned into 95% training and 5% test samples at 100 to 1000 epochs. The classifier was constructed using the training datasets while its efficiency was assessed on the test sample using the Misclassification Error Rate, Sensitivity, Specificity, and Receiver Operating Characteristic Analysis. The GA and DBN were implemented using Caret and Deepnet (R statistical packages). The proposed GADBN method was implemented using simulated and real-life datasets, which yielded out-of-bag average classification accuracy of 98.8% and 93.1% respectively. The proposed GADBN outperformed the other existing classifiers;and the GADBN method was more efficient with a smaller Misclassification Error Rate (MER) for Leukaemia 1 (0.18), Prostate 1 (0.35) and Prostate 3 (0.09) datasets than some of the existing methods under various performance indices considered. The proposed model is a powerful and effective instrument for identifying useful features in microarray cancer data and classifying the cancer types accordingly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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Data Augmentation Using Conditional Generative Adversarial Network (Cgan) for Android Malware Binary and Multi-Class Classification 15

Data Augmentation Using Conditional Generative Adversarial N...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Othman, Fawaz Mohammad Hayel Al-Rimy, Bander Ali Saleh Almalki, Sultan Ahmed Alghamdi, Tami Abdulrahman Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia School of Computing University of Portsmouth Portsmouth United Kingdom Applied College Najran University Najran66462 Saudi Arabia Al-Baha University Faculty of Computing and Information Al-Baha1988 Saudi Arabia

ISBN: (纸本)9798331507695

In the field of mobile malware detection and classification, various datasets have been constructed, particularly for Android operating systems, which experience the highest average of malware attacks. Traditional detection and classification methods that rely on malware signatures are becoming increasingly ineffective, especially against zero-day malware, necessitating smart detection methods. Machine learning models have evolved to address this problem as new malware becomes more sophisticated. To improve these models for malware classification and detection, new datasets are required to enhance accuracy. However, constructing these datasets manually is tedious and complicates the prediction of zero-day malware patterns. Additionally, there is a lack of comprehensive Android malware datasets to improve detection accuracy. In this study, a new synthetic multiclass Android malware dataset was generated from one of the most recent datasets, CCCS-CIC-AndMal2020, using two approaches as conditional inputs to a Conditional Generative Adversarial Network (CGAN) model: binary classification and multiclassification. A machine learning classification model was developed to evaluate the results of the two approaches by combining the generated data with the original data to test the quality of the generated data. The multiclassification approach achieved low accuracy, while the binary classification approach achieved 85% accuracy using LightGBM and 78% using Random Forest, compared to the original data accuracy, which was 85% using both LightGBM and Random Forest. © 2025 IEEE.

关键词： Generative adversarial networks

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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AI Modeling and Water Quality Sensing Technique Proffers Water Security: An Open Review 3

AI Modeling and Water Quality Sensing Technique Proffers Wat...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Haidar, Sk Wasim Sutherlin Subitha, G. Sarmila, J. University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari District India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Chunkankadai India

ISBN: (纸本)9798331509675

Water Quality Sensors (WQSs) are becoming a promised tool in water quality data assessment and scientific value of aquatic structure. Such sensors are broadly used to produce live results by evaluating major water quality parameters and components. Artificial intelligence (AI) based WQSs and machine learning (ML) algorithms primarily enable long term operations for real time detention of water quality information at different time periods. Messaging services, alarms, alerts and other proper warnings are given to water quality managers or assessing communities while the quality deterioration occurs at the particular water quality checking sites. A good number of impressive challenges are identified in hosting the variety of sensors to manage, warehouse and analyze incessant water quality information that they generate periodically. This paper places interests in highlighting lack of suitable assessment standards and determine logistical deliberations for fruitful implementations together with transportation, storage services and appropriate trustworthy communication. The main objective is to identify the designs targeting water security that support to monitor, reassure and protect the water resources all the way through uninterrupted quality supervision in addition to AI features. This open review also discusses a variety of applications in water quality sensors, AI modeling, electronic tongues and indices, hence results the knowledge of improvement required for upgrading the quality in water resources through computer technologies and models. © 2025 IEEE.

关键词： Electronic tongues

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Designing a hybrid neural network framework for real-time anomaly detection in cybersecurity applications

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Jain, Aaditya Ead, Waleed M. Alshahrani, Mohammed Altamimi, Jawaher Suliman Aklan, Fawaz Rihan, Shaza Dawood College of Computing Sciences and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Computers Department Applied College Najran University Najran Saudi Arabia Applied College Najran University Najran Saudi Arabia Department of Computer Science Faculty of Applied Science Taiz University Taiz P.O. Box 6803 Taiz Yemen

As cybersecurity is becoming a prominent domain, it is an imperative to provide real-time anomaly detection for early threat detection and to keep networks safe. In this paper, an optimized hybrid neural network-framework is proposed to detect anomalies at several attack vectors with high efficacy. The framework comprises convolutional neural network equipped with long-short term memory (CNN-LSTM) for temporal and spatial representation of data. As the system accommodates new data and keeps on learning, it adjusts itself becoming a significant mean in counteracting against emerging threats with less latency in detection. In order to verify the efficiency and robustness of the proposed framework, a series of real-time simulation is designed using key performance metrics such as detection rate, false alarm rate and processing time. The results show higher improvement in accuracy and speed of anomaly detection, which makes the framework a useful instrument to reinforce cybersecurity defense during real-time scenarios. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Anomaly detection Convolutional neural network (CNN) Cybersecurity Hybrid approach Long-short term memory (LSTM)

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Machine Learning-Driven QoT Prediction for Enhanced Optical Networks in DWDM system

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IEEE Access 2025年 13卷 80445-80460页

作者： Sakthivel, Sudha Belgaum, Mohammad Riyaz Sajak, Aznida Abu Bakar Alam, Muhammad Mansoor Su'ud, Mazliham Mohd Universiti Kuala Lumpur Advanced Telecommunication Technology Research Cluster Malaysia University of Technology and Applied Sciences Muscat133 Oman Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Arab Open University Faculty of Computer Studies 18211 Bahrain Riphah International University Faculty of Computing Islamabad46000 Pakistan

This research demonstrated the machine learning (ML) classifiers with regression learning to improve an optical system’s quality of transmission (QoT). In Optical Communication, the data can be communicated from source to destination through the established lightpaths. However, as the signal traverses the optical links and devices, its QoT may deteriorate due to various impairments. The QoT is an essential component that determines the connectivity of an optical network. Therefore, ensuring a QoT guarantee is necessary to establish a successful lightpath. Predicting the QoT before establishing lightpaths can guide the routing and allocation of resources required for the lightpaths. In this research, using ML models an appropriate QoT analytical prediction model is developed computationally. Simulations were conducted at a 10 Gbps data rate per channel for 64-channel DWDM systems. The proposed model significantly improves in detecting fiber nonlinearity, and performance was studied using Q-factor, BER, and noise power. The results indicate that the SVM-based classifier with regression learning performs better than any other classifiers discussed in this research. This study assesses the efficiency of the proposed ML models in predicting the QoT for established lightpaths. Results indicate that all the ML classifiers with Regression models can accurately predict the transmission quality for over 90% of lightpaths. However, the proposed SVM-based classifier with a regression model demonstrates superior generalization, with a nearly perfect QoT prediction rate of around 99% for the established lightpaths. In the network planning stage, residual margins are added to compensate for inaccuracies, which ensures accurate signal reception. The proposed ML model achieved a lightpath residual margin with a 0.7 dB error. © 2013 IEEE.

关键词： Fiber to the x

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